




'''simple docstring'''


import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available





class  __lowercase    (	__magic_name__			):
   _a    						=   ["""image_processor""", """tokenizer"""]
   _a    						=   """OwlViTImageProcessor"""
   _a    						=   ("""CLIPTokenizer""", """CLIPTokenizerFast""")







   def __init__(						self   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				**UpperCamelCase   )					->     int:
     __a								=       None
     if "feature_extractor" in kwargs:
       warnings.warn(
           'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
           ' instead.'   ,				UpperCamelCase   ,				)
       __a								=       kwargs.pop('feature_extractor'   )

     __a								=       image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
       raise ValueError('You need to specify an `image_processor`.'   )
     if tokenizer is None:
       raise ValueError('You need to specify a `tokenizer`.'   )

     super().__init__(UpperCamelCase   ,				UpperCamelCase   )







   def __call__(						self   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase="max_length"   ,				UpperCamelCase="np"   ,				**UpperCamelCase   )					->     Union[str, Any]:

     if text is None and query_images is None and images is None:
       raise ValueError(
           'You have to specify at least one text or query image or image. All three cannot be none.'   )

     if text is not None:
       if isinstance(UpperCamelCase   ,				UpperCamelCase   ) or (isinstance(UpperCamelCase   ,				UpperCamelCase   ) and not isinstance(text[0]   ,				UpperCamelCase   )):
         __a								=       [self.tokenizer(UpperCamelCase   ,				padding=UpperCamelCase   ,				return_tensors=UpperCamelCase   ,				**UpperCamelCase   )]

       elif isinstance(UpperCamelCase   ,				UpperCamelCase   ) and isinstance(text[0]   ,				UpperCamelCase   ):
         __a								=       []

         # Maximum number of queries across batch
         __a								=       max([len(UpperCamelCase   ) for t in text]   )

         # Pad all batch samples to max number of text queries
         for t in text:
           if len(UpperCamelCase   ) != max_num_queries:
             __a								=       t + [' '] * (max_num_queries - len(UpperCamelCase   ))

           __a								=       self.tokenizer(UpperCamelCase   ,				padding=UpperCamelCase   ,				return_tensors=UpperCamelCase   ,				**UpperCamelCase   )
           encodings.append(UpperCamelCase   )
       else:
         raise TypeError('Input text should be a string, a list of strings or a nested list of strings'   )

       if return_tensors == "np":
         __a								=       np.concatenate([encoding['input_ids'] for encoding in encodings]   ,				axis=0   )
         __a								=       np.concatenate([encoding['attention_mask'] for encoding in encodings]   ,				axis=0   )

       elif return_tensors == "jax" and is_flax_available():
         import jax.numpy as jnp

         __a								=       jnp.concatenate([encoding['input_ids'] for encoding in encodings]   ,				axis=0   )
         __a								=       jnp.concatenate([encoding['attention_mask'] for encoding in encodings]   ,				axis=0   )

       elif return_tensors == "pt" and is_torch_available():
         import torch

         __a								=       torch.cat([encoding['input_ids'] for encoding in encodings]   ,				dim=0   )
         __a								=       torch.cat([encoding['attention_mask'] for encoding in encodings]   ,				dim=0   )

       elif return_tensors == "tf" and is_tf_available():
         import tensorflow as tf

         __a								=       tf.stack([encoding['input_ids'] for encoding in encodings]   ,				axis=0   )
         __a								=       tf.stack([encoding['attention_mask'] for encoding in encodings]   ,				axis=0   )

       else:
         raise ValueError('Target return tensor type could not be returned'   )

       __a								=       BatchEncoding()
       __a								=       input_ids
       __a								=       attention_mask

     if query_images is not None:
       __a								=       BatchEncoding()
       __a								=       self.image_processor(
           UpperCamelCase   ,				return_tensors=UpperCamelCase   ,				**UpperCamelCase   ).pixel_values
       __a								=       query_pixel_values

     if images is not None:
       __a								=       self.image_processor(UpperCamelCase   ,				return_tensors=UpperCamelCase   ,				**UpperCamelCase   )

     if text is not None and images is not None:
       __a								=       image_features.pixel_values
       return encoding
     elif query_images is not None and images is not None:
       __a								=       image_features.pixel_values
       return encoding
     elif text is not None or query_images is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**UpperCamelCase   )   ,				tensor_type=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				*UpperCamelCase   ,				**UpperCamelCase   )					->     List[str]:
     return self.image_processor.post_process(*UpperCamelCase   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				*UpperCamelCase   ,				**UpperCamelCase   )					->     Optional[int]:
     return self.image_processor.post_process_object_detection(*UpperCamelCase   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				*UpperCamelCase   ,				**UpperCamelCase   )					->     int:
     return self.image_processor.post_process_image_guided_detection(*UpperCamelCase   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				*UpperCamelCase   ,				**UpperCamelCase   )					->     Any:
     return self.tokenizer.batch_decode(*UpperCamelCase   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				*UpperCamelCase   ,				**UpperCamelCase   )					->     Dict:
     return self.tokenizer.decode(*UpperCamelCase   ,				**UpperCamelCase   )







   @property
   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,				UpperCamelCase   ,				)
     return self.image_processor_class







   @property
   def 		UpperCamelCase__      (						self   )					->     List[str]:
     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,				UpperCamelCase   ,				)
     return self.image_processor









'''simple docstring'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TrajectoryTransformerModel",
      "TrajectoryTransformerPreTrainedModel",
      "load_tf_weights_in_trajectory_transformer",
  ]


if TYPE_CHECKING:
  from .configuration_trajectory_transformer import (
      TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TrajectoryTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trajectory_transformer import (
        TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TrajectoryTransformerModel,
        TrajectoryTransformerPreTrainedModel,
        load_tf_weights_in_trajectory_transformer,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



